From our homes, vehicles, and mobile devices, to revolutionary business developments and the Fourth Industrial Revolution, AI has left science fiction films for real life. Artificial intelligence and machine learning are rapidly revolutionizing everything in industry, including warehouse management and supply chain logistics.
The smart warehouse combines various interconnected technologies to form an ecosystem in which the entire business, from delivery to delivery, is managed by AI. Goods enter the warehouse, are identified and sorted, processed, packed and delivered for shipment, all automatically and with a minimum margin of error. Warehouse management will become more flexible, more responsive to the logistical needs of material items and personnel, and more scalable in terms of finding new solutions to increase volume and product flow.
Fully automated warehousing has yet to be achieved, but here are some of the ways AI is transforming warehouse management:
- Communication.
Automated systems using AI and machine learning algorithms can interact at exponentially faster rates than humans. Many warehouse operations are already automated, but the introduction of IoT-enabled devices into these processes will significantly increase their speed and accuracy. Wireless data transmission using IO Link and Fieldbus means that all elements of the system can communicate with each other using a dialogue that includes monitoring and controlling the system. Deep learning processes allow machines to continuously analyze the data streams generated by these components, allowing them to make real-time adjustments and improvements to the integrated warehouse management system.
- Warehouse logistics.
Another way AI is transforming warehouse management is by optimizing logistics, that is, counting the number of pallets or packages that need to be moved on any given day, and how much equipment is required to handle that move. While this calculation previously used variables such as operator skill level and SKU (Inventory Counting Unit), machine learning algorithms can predict and manage stock movement in detail to fine-tune material handling. Thus, operator errors and processing time can be reduced with a corresponding increase in overall efficiency and productivity.
- Performance.
AI will continue to transform warehouse management by increasing the productivity of picking and packaging processes, while machine learning will enable managers to leverage the efficiency of their most productive pickers to develop a fully integrated system-centric solution. Slot software products already provide an interface that includes operating rules to be implemented in a smart warehouse, offering a recommended SKU strategy based on sales history and forecasts. While people are still using personal knowledge and experience to adjust their time allocation strategy, this will increasingly be phased out in favor of machine learning algorithms.
- Inventory.
AI will also transform warehouse management, freeing up money previously spent on inventory management for other business growth opportunities. RFID (Radio Frequency Identification) is replacing paper and barcode scanners to organize and control inventory, track products with digital tags, and provide more accurate inventory control. Since the system uses radio waves to transmit data, RFID scanners do not need direct line-of-sight control, but are simply pointed in the general direction of the product to identify it and guide it through the warehouse. This aspect of the smart warehouse can also be linked to the AI CPU, adjusting the speed and volume of order processing to increase overall productivity.
- Robots.
Machine learning algorithms can help robots select the most efficient picking and distribution routes and determine the best type of packaging based on product size, quantity, weight, and product type. Some machines can now even pack products using AI to optimize space and materials.
- Salary.
Perhaps the most controversial way that AI is transforming warehouse management is to cut payroll costs, although this may initially be offset by the necessary technology investments. At the current stage of development, robot assistance only affects existing operations as a productivity aid, but AI can and will continue to improve machine handling capabilities, with 30% of warehouse jobs by 2030 being fully automated. Large e-commerce businesses assume that increasing their automation will create jobs by expanding the overall scale of their business activities.